system

Abstract

The system according to this embodiment aims to analyze audio or text and output it as a web comic. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, and an output unit. The reception unit receives voice or text input. The generation unit analyzes the information received by the reception unit and generates an illustration. The output unit outputs the illustration generated by the generation unit as a web comic.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it cannot be said that illustrations are sufficiently automatically generated from voices or texts and output as web comics, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze voices or texts and output them as web comics.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a generation unit, and an output unit. The reception unit receives an input of voices or texts. The generation unit analyzes the information received by the reception unit and generates illustrations. The output unit outputs the illustrations generated by the generation unit as web comics. [Effects of the Invention]

[0007] The system according to this embodiment can analyze audio or text and output it as a web comic. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The web manga generation system according to an embodiment of the present invention is a system in which an AI automatically recognizes a user's experiences or things they want to convey, summarized in voice or text, and creates a web manga from it. This system is extremely useful for people who want to share their experiences and thoughts on social media but do not want to show their face, or for people who do not have the time or talent to draw illustrations. Because the generating AI creates the illustrations, users can engage in social media activities without revealing their identity, and anyone can become a manga artist. In addition to distribution-related content, illustrating everyday conversations makes the situation easier to understand. For example, using it in messaging apps can facilitate communication between the elderly and younger generations. As for monetization methods, a free trial (up to a few times) followed by a subscription model is a possible approach. First, the user inputs their experiences or things they want to convey in voice or text. At this time, the user can freely input their experiences and thoughts. Next, the generating AI analyzes the input information and generates a web manga. The generating AI generates the most suitable illustration based on the input information and outputs it as a web manga. This allows users to easily create manga. Furthermore, the generated manga is output to the web so that users can easily view it. To monetize the service, a subscription model will be implemented, allowing users to access it for free a certain number of times. After that, users will be able to continue using the service by paying a monthly fee. This will enable the web manga generation system to easily express users' experiences and what they want to convey as manga, facilitating communication on social media and messaging apps.

[0029] The web manga generation system according to this embodiment comprises a reception unit, a generation unit, and an output unit. The reception unit accepts audio or text input. For example, if the user inputs an experience or something they want to convey via audio, the reception unit can convert the audio to text using speech recognition technology. Furthermore, if the user inputs an experience or something they want to convey via text, the reception unit can analyze the text using natural language processing technology. For example, the reception unit can accept MP3, WAV, and other formats as audio files. In addition, the reception unit can support multiple languages, such as Japanese and English, as the language of the text. The generation unit uses a generation AI to analyze the information received by the reception unit and generate an illustration. For example, the generation unit can use deep learning technology to generate the optimal illustration based on the input information. The generation unit can also adjust the style and resolution of the generated illustration. For example, the generation unit can generate illustrations according to the user's preferences, such as a realistic style or a cartoon style. The output unit outputs the illustration generated by the generation unit as a web manga. The output unit can, for example, generate HTML and CSS for displaying the generated illustrations on a web page. The output unit can also save the generated illustrations in PDF or image format. For example, the output unit can automatically adjust the number of pages and panel layout of a web comic to make it easy for users to view. As a result, the web comic generation system according to this embodiment can generate a web comic based on user input of their experiences or what they want to convey, either in voice or text.

[0030] The reception desk accepts input in the form of voice or text. Specifically, when a user inputs their experiences or what they want to convey via voice, speech recognition technology can be used to convert the voice into text. The speech recognition technology uses a deep learning-based voice model, enabling highly accurate speech recognition. For example, when a user inputs voice using a smartphone or microphone, the voice data is sent to the reception desk in MP3 or WAV format. The reception desk analyzes this voice data in real time and converts it into text data. Furthermore, pre-processing such as noise reduction and speech normalization is performed during the speech recognition process to improve recognition accuracy. For text input, natural language processing technology is used to analyze the text. Natural language processing technology includes morphological analysis, grammatical analysis, and semantic analysis, which allows for accurate understanding of the content of the input text. For example, when a user inputs text in Japanese or English, the text is divided into words by morphological analysis, and the sentence structure is analyzed by grammatical analysis. Furthermore, the meaning of the text is understood through semantic analysis, and the user's intent is grasped. Based on these analysis results, the reception desk can accurately understand the user's experiences and what they want to convey and pass it on to the next processing step. This allows the reception desk to receive voice and text input with high accuracy and accurately understand the user's intent.

[0031] The generation unit uses a generation AI to analyze information received by the reception unit and generate illustrations. The generation AI employs a deep learning-based model, enabling the generation of high-quality illustrations. Specifically, the generation AI analyzes the input text data and generates the most suitable illustration based on its content. For example, if a user inputs "a fun barbecue scene on the beach," the generation AI analyzes the content and generates an illustration depicting a beach scene and a barbecue. The generation AI has previously learned from a large amount of illustration data and can generate new illustrations based on the learning results. Furthermore, the generation unit can adjust the style and resolution of the generated illustrations. For example, if a user desires a realistic style, the generation AI will generate an illustration in that style. It can also generate illustrations in cartoon or anime styles, according to the user's preference. Regarding resolution, the generation unit can generate high-resolution illustrations, enabling support for printing and large-screen display. By utilizing these functions, the generation unit can quickly generate high-quality illustrations based on user input.

[0032] The output unit outputs the illustrations generated by the generation unit as a web comic. Specifically, it can generate HTML and CSS for displaying the generated illustrations on a web page. HTML defines the structure of the web page, and CSS defines its style. The output unit uses these technologies to create a web page that effectively displays the generated illustrations. For example, the output unit places the illustrations in the appropriate positions and adds interface elements such as text and buttons. The output unit can also save the generated illustrations in PDF or image format. PDF format is suitable for printing and offline viewing, while image format is suitable for sharing on social media and email. Furthermore, the output unit can automatically adjust the number of pages and panel layout of the web comic to make it easy for users to view. For example, the output unit performs appropriate panel layout based on the content of the illustrations and adjusts the number of pages. It can also employ responsive design to accommodate users viewing on different devices such as smartphones and tablets. In this way, the output unit can output the generated illustrations in various formats, making it easy for users to view and share them.

[0033] The management department can manage subscription models for monetization. For example, the management department can set it up so that users can use the service for free up to a certain number of times, and then continue using the service by paying a monthly fee thereafter. The management department needs to clarify the types and management methods of subscription models. For example, these include monthly billing, annual billing, and freemium models. The management department can monitor user usage and suggest subscription renewals at the appropriate time. For example, the management department can send notifications to users when their subscription is about to expire and prompt them to renew. This allows for the management of subscription models for monetization.

[0034] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display phrases and expressions that the user has frequently used in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can suggest input methods related to specific themes based on the user's past input history. In this way, the optimal input method can be suggested by analyzing the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input data into a generating AI and have the generating AI suggest the optimal input method.

[0035] The reception unit can automatically remove background noise during voice input to obtain clear audio. For example, when a user is using voice input in a noisy place such as a cafe, the reception unit can automatically remove background noise. The reception unit can also filter out wind noise and car noise when a user is using voice input outdoors. Furthermore, the reception unit can remove sounds from televisions and radios when a user is using voice input at home. This allows for the acquisition of clear audio by removing background noise. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input audio data into a generating AI and have the generating AI perform background noise removal.

[0036] The reception unit can prioritize retrieving highly relevant information by considering the user's geographical location when voice or text input is received. For example, if the user is traveling, the reception unit can prioritize retrieving information related to their current location. Similarly, if the user is at home, the reception unit can prioritize retrieving information about nearby events. Furthermore, if the user is in a specific location, the reception unit can prioritize retrieving historical and cultural information related to that location. This allows for the prioritization of highly relevant information by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For instance, the reception unit can input the user's geographical location information into a generating AI and have the generating AI retrieve highly relevant information.

[0037] The reception unit can analyze the user's social media activity and retrieve relevant information when voice or text input is received. For example, the reception unit can prioritize retrieving information related to the user's most recent posts. It can also retrieve information related to posts from accounts the user follows. Furthermore, the reception unit can retrieve information related to the activities of groups and communities the user participates in. In this way, relevant information can be obtained by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI retrieve relevant information.

[0038] The generation unit can adjust the level of detail in the illustration based on the importance of the input content during generation. For example, in important scenes, the generation unit can depict detailed backgrounds and character expressions. In less important scenes, the generation unit can use simple backgrounds and character silhouettes. Furthermore, the generation unit can draw specific parts in detail to emphasize highly important information. In this way, important scenes can be emphasized by adjusting the level of detail in the illustration based on the importance of the input content. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the importance of the input content into the generation AI and have the generation AI perform the adjustment of the level of detail in the illustration.

[0039] The generation unit can apply different illustration generation algorithms depending on the category of the input content during generation. For example, in the case of the comedy category, the generation unit can apply an algorithm that emphasizes humorous expressions. In the case of the drama category, the generation unit can also apply an algorithm that emphasizes emotional expression. Furthermore, in the case of the action category, the generation unit can apply an algorithm that emphasizes dynamic scenes. By applying different illustration generation algorithms depending on the category of the input content, more appropriate illustrations can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the category of the input content into the generation AI and have the generation AI execute the application of the illustration generation algorithm.

[0040] The generation unit can determine the priority of illustrations based on the submission timing of the input content during generation. For example, the generation unit can prioritize processing input content with an approaching deadline. It can also postpone processing input content with a later submission deadline. Furthermore, the generation unit can dynamically adjust the processing priority according to the submission timing. This allows for deadline-based processing by determining the priority of illustrations based on the submission timing of the input content. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the submission timing of the input content into the generation AI and have the generation AI determine the priority of the illustrations.

[0041] The generation unit can adjust the order of illustrations based on the relevance of the input content during generation. For example, the generation unit can place important scenes first to clarify the flow of the story. It can also postpone scenes with low relevance. Furthermore, to maintain the consistency of the story, the generation unit can place highly relevant scenes consecutively. In this way, the consistency of the story can be maintained by adjusting the order of illustrations based on the relevance of the input content. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the relevance of the input content into the generation AI and have the generation AI perform the adjustment of the order of illustrations.

[0042] The output unit can select the optimal display method by referring to the user's past browsing history when outputting. For example, the output unit can prioritize displaying manga in styles that the user has previously enjoyed reading. The output unit can also adjust the display method based on the genres of manga the user has previously viewed. Furthermore, the output unit can select the optimal display layout from the user's past browsing history. In this way, the optimal display method can be selected by referring to the user's past browsing history. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the user's past browsing history data into a generating AI and have the generating AI select the optimal display method.

[0043] The output unit can select the optimal display format when outputting, taking into account the user's device information. For example, if the user is using a smartphone, the output unit can provide a display format that matches the screen size. Furthermore, if the user is using a tablet, the output unit can provide a display format optimized for a larger screen. Additionally, if the user is using a desktop PC, the output unit can provide a display format optimized for a larger screen. This allows for the selection of the optimal display format by considering the user's device information. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the user's device information into a generating AI and have the generating AI select the optimal display format.

[0044] The output unit can select the optimal display method when outputting, taking into account the user's geographical location information. For example, if the user is traveling, the output unit can prioritize displaying information related to the user's current location. Similarly, if the user is at home, the output unit can prioritize displaying information about nearby events. Furthermore, if the user is in a specific location, the output unit can prioritize displaying historical and cultural information related to that location. This allows the system to select the optimal display method by considering the user's geographical location information. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For instance, the output unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal display method.

[0045] The output unit can analyze the user's social media activity and select the optimal display method at the time of output. For example, the output unit can prioritize displaying information related to the user's recent posts. It can also display information related to posts from accounts the user follows. Furthermore, the output unit can display information related to the activities of groups and communities the user participates in. In this way, the optimal display method can be selected by analyzing the user's social media activity. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the user's social media activity data into a generating AI and have the generating AI select the optimal display method.

[0046] The management department can propose the optimal plan by referring to the user's past usage history during management. For example, the management department can propose a plan that includes features the user has frequently used in the past. The management department can also propose updates to plans the user has used in the past. Furthermore, the management department can customize and propose the optimal plan based on the user's past usage history. In this way, the optimal plan can be proposed by referring to the user's past usage history. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input the user's past usage history data into a generating AI and have the generating AI propose the optimal plan.

[0047] The management department can propose the optimal plan during management, taking into account the user's geographical location information. For example, if the user lives in an urban area, the management department can propose a plan suited to urban areas. Similarly, if the user lives in a suburban area, the management department can propose a plan suited to suburban areas. Furthermore, if the user lives in a specific region, the management department can propose a plan tailored to that region. This allows the management department to propose the optimal plan by considering the user's geographical location information. Some or all of the above processing in the management department may be performed using AI, for example, or without AI. For example, the management department can input the user's geographical location information into a generating AI and have the generating AI propose the optimal plan.

[0048] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0049] The reception desk can automatically search for and suggest relevant images and videos based on the user's input. For example, if a user enters a travel experience, the reception desk can suggest images of scenery and videos of tourist spots at that travel destination. Similarly, if a user enters a cooking recipe, the reception desk can suggest videos demonstrating the cooking process and images of the finished dish. Furthermore, if a user enters a sports experience, the reception desk can suggest highlight videos and related images of that sport. This allows for the generation of richer content by providing visual information relevant to the user's input.

[0050] The administration department can send personalized notifications based on user usage. For example, if a user frequently uses a particular feature, it can notify them of new updates or tutorials related to that feature. It can also notify users of special offers or discounts to encourage re-use if they haven't used the service for a certain period. Furthermore, it can send reminders to users renew their subscriptions as they approach their expiration date. This allows for improved user engagement by sending appropriate notifications tailored to user usage.

[0051] The reception desk can analyze a user's past input history and suggest the most suitable input template. For example, it can automatically generate and suggest templates that include phrases and expressions the user has frequently used in the past. It can also suggest templates appropriate for a specific theme when the user is making input related to that theme. Furthermore, it can suggest the most suitable template based on the input method the user has used in the past (voice, text, etc.). This allows for efficient input by analyzing the user's past input history.

[0052] The reception desk can analyze the user's voice tone and speed during voice input and suggest the optimal input method. For example, if the user is speaking quickly, the reception desk can suggest speaking more slowly. Similarly, if the user is speaking in a low voice, it can suggest speaking more clearly. Furthermore, if the user is speaking with an emotional tone, it can suggest an input method that reflects that emotion. By analyzing the user's voice tone and speed during voice input, it can support more effective input.

[0053] The reception desk can suggest relevant information based on the user's interests when they input voice or text. For example, if the user is interested in sports, it can suggest sports-related news and articles. If the user is interested in cooking, it can suggest recipes and cooking tips. Furthermore, if the user is interested in travel, it can suggest information about travel destinations and tourist attractions. In this way, by considering the user's interests, it can provide relevant information and improve the user experience.

[0054] The reception desk can adjust the input content, whether voice or text, taking into account the user's health condition. For example, if the user is tired, it can suggest a simpler input method. If the user is stressed, it can suggest a more relaxing input method. Furthermore, if the user is unwell, it can encourage them to input within their limits. This allows for the provision of more appropriate input methods and reduces the user's burden by considering their health condition.

[0055] The generation unit can adjust the style of the illustration based on the theme of the input content during generation. For example, if the theme is fantasy, it can generate an illustration using a fantastical style. If the theme is horror, it can generate an illustration using dark colors and a style that emphasizes fear. Furthermore, if the theme is comedy, it can generate an illustration using bright colors and a humorous style. In this way, by adjusting the style of the illustration based on the theme of the input content, it is possible to generate a more appropriate illustration.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The reception desk accepts voice or text input. If the user inputs their experience or what they want to convey via voice, speech recognition technology is used to convert the voice into text. If the user inputs their experience or what they want to convey via text, natural language processing technology is used to analyze the text. The reception desk accepts audio files in formats such as MP3 and WAV, and supports multiple languages ​​for text input, including Japanese and English. Step 2: The generation unit uses generation AI to analyze the information received by the reception unit and generate an illustration. The generation unit uses deep learning technology to generate the optimal illustration based on the input information and adjusts the style and resolution of the generated illustration. For example, it generates illustrations according to the user's preference, such as a realistic style or a cartoon style. Step 3: The output unit outputs the illustrations generated by the generation unit as a web comic. The output unit also generates HTML and CSS for displaying the generated illustrations on a web page, and can save them in PDF or image format. Furthermore, it automatically adjusts the number of pages and panel layout of the web comic to make it easy for users to view.

[0058] (Example of form 2) The web manga generation system according to an embodiment of the present invention is a system in which an AI automatically recognizes a user's experiences or things they want to convey, summarized in voice or text, and creates a web manga from it. This system is extremely useful for people who want to share their experiences and thoughts on social media but do not want to show their face, or for people who do not have the time or talent to draw illustrations. Because the generating AI creates the illustrations, users can engage in social media activities without revealing their identity, and anyone can become a manga artist. In addition to distribution-related content, illustrating everyday conversations makes the situation easier to understand. For example, using it in messaging apps can facilitate communication between the elderly and younger generations. As for monetization methods, a free trial (up to a few times) followed by a subscription model is a possible approach. First, the user inputs their experiences or things they want to convey in voice or text. At this time, the user can freely input their experiences and thoughts. Next, the generating AI analyzes the input information and generates a web manga. The generating AI generates the most suitable illustration based on the input information and outputs it as a web manga. This allows users to easily create manga. Furthermore, the generated manga is output to the web so that users can easily view it. To monetize the service, a subscription model will be implemented, allowing users to access it for free a certain number of times. After that, users will be able to continue using the service by paying a monthly fee. This will enable the web manga generation system to easily express users' experiences and what they want to convey as manga, facilitating communication on social media and messaging apps.

[0059] The web manga generation system according to this embodiment comprises a reception unit, a generation unit, and an output unit. The reception unit accepts audio or text input. For example, if the user inputs an experience or something they want to convey via audio, the reception unit can convert the audio to text using speech recognition technology. Furthermore, if the user inputs an experience or something they want to convey via text, the reception unit can analyze the text using natural language processing technology. For example, the reception unit can accept MP3, WAV, and other formats as audio files. In addition, the reception unit can support multiple languages, such as Japanese and English, as the language of the text. The generation unit uses a generation AI to analyze the information received by the reception unit and generate an illustration. For example, the generation unit can use deep learning technology to generate the optimal illustration based on the input information. The generation unit can also adjust the style and resolution of the generated illustration. For example, the generation unit can generate illustrations according to the user's preferences, such as a realistic style or a cartoon style. The output unit outputs the illustration generated by the generation unit as a web manga. The output unit can, for example, generate HTML and CSS for displaying the generated illustrations on a web page. The output unit can also save the generated illustrations in PDF or image format. For example, the output unit can automatically adjust the number of pages and panel layout of a web comic to make it easy for users to view. As a result, the web comic generation system according to this embodiment can generate a web comic based on user input of their experiences or what they want to convey, either in voice or text.

[0060] The reception desk accepts input in the form of voice or text. Specifically, when a user inputs their experiences or what they want to convey via voice, speech recognition technology can be used to convert the voice into text. The speech recognition technology uses a deep learning-based voice model, enabling highly accurate speech recognition. For example, when a user inputs voice using a smartphone or microphone, the voice data is sent to the reception desk in MP3 or WAV format. The reception desk analyzes this voice data in real time and converts it into text data. Furthermore, pre-processing such as noise reduction and speech normalization is performed during the speech recognition process to improve recognition accuracy. For text input, natural language processing technology is used to analyze the text. Natural language processing technology includes morphological analysis, grammatical analysis, and semantic analysis, which allows for accurate understanding of the content of the input text. For example, when a user inputs text in Japanese or English, the text is divided into words by morphological analysis, and the sentence structure is analyzed by grammatical analysis. Furthermore, the meaning of the text is understood through semantic analysis, and the user's intent is grasped. Based on these analysis results, the reception desk can accurately understand the user's experiences and what they want to convey and pass it on to the next processing step. This allows the reception desk to receive voice and text input with high accuracy and accurately understand the user's intent.

[0061] The generation unit uses a generation AI to analyze information received by the reception unit and generate illustrations. The generation AI employs a deep learning-based model, enabling the generation of high-quality illustrations. Specifically, the generation AI analyzes the input text data and generates the most suitable illustration based on its content. For example, if a user inputs "a fun barbecue scene on the beach," the generation AI analyzes the content and generates an illustration depicting a beach scene and a barbecue. The generation AI has previously learned from a large amount of illustration data and can generate new illustrations based on the learning results. Furthermore, the generation unit can adjust the style and resolution of the generated illustrations. For example, if a user desires a realistic style, the generation AI will generate an illustration in that style. It can also generate illustrations in cartoon or anime styles, according to the user's preference. Regarding resolution, the generation unit can generate high-resolution illustrations, enabling support for printing and large-screen display. By utilizing these functions, the generation unit can quickly generate high-quality illustrations based on user input.

[0062] The output unit outputs the illustrations generated by the generation unit as a web comic. Specifically, it can generate HTML and CSS for displaying the generated illustrations on a web page. HTML defines the structure of the web page, and CSS defines its style. The output unit uses these technologies to create a web page that effectively displays the generated illustrations. For example, the output unit places the illustrations in the appropriate positions and adds interface elements such as text and buttons. The output unit can also save the generated illustrations in PDF or image format. PDF format is suitable for printing and offline viewing, while image format is suitable for sharing on social media and email. Furthermore, the output unit can automatically adjust the number of pages and panel layout of the web comic to make it easy for users to view. For example, the output unit performs appropriate panel layout based on the content of the illustrations and adjusts the number of pages. It can also employ responsive design to accommodate users viewing on different devices such as smartphones and tablets. In this way, the output unit can output the generated illustrations in various formats, making it easy for users to view and share them.

[0063] The management department can manage subscription models for monetization. For example, the management department can set it up so that users can use the service for free up to a certain number of times, and then continue using the service by paying a monthly fee thereafter. The management department needs to clarify the types and management methods of subscription models. For example, these include monthly billing, annual billing, and freemium models. The management department can monitor user usage and suggest subscription renewals at the appropriate time. For example, the management department can send notifications to users when their subscription is about to expire and prompt them to renew. This allows for the management of subscription models for monetization.

[0064] The reception unit can estimate the user's emotions and filter the input content based on the estimated emotions. For example, if the user is sad, the reception unit can filter out input content containing negative expressions and convert them into positive expressions. Similarly, if the user is excited, the reception unit can suppress overly emotional expressions and convert them into calmer expressions. Furthermore, if the user is tired, the reception unit can simplify redundant expressions and convert them into easy-to-read content. This allows for more appropriate input by filtering the input content based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input the user's voice data into a generative AI and have the generative AI perform emotion estimation.

[0065] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display phrases and expressions that the user has frequently used in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can suggest input methods related to specific themes based on the user's past input history. In this way, the optimal input method can be suggested by analyzing the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input data into a generating AI and have the generating AI suggest the optimal input method.

[0066] The reception unit can automatically remove background noise during voice input to obtain clear audio. For example, when a user is using voice input in a noisy place such as a cafe, the reception unit can automatically remove background noise. The reception unit can also filter out wind noise and car noise when a user is using voice input outdoors. Furthermore, the reception unit can remove sounds from televisions and radios when a user is using voice input at home. This allows for the acquisition of clear audio by removing background noise. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input audio data into a generating AI and have the generating AI perform background noise removal.

[0067] The reception unit can estimate the user's emotions and prioritize input based on the estimated emotions. For example, if the user is excited, the reception unit can prioritize inputting important information. If the user is relaxed, the reception unit can prioritize inputting detailed information. Furthermore, if the user is in a hurry, the reception unit can prioritize inputting concise information. This allows for the priority of input based on the user's emotions, ensuring that important information is prioritized. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's voice data into a generative AI and have the generative AI perform emotion estimation.

[0068] The reception unit can prioritize retrieving highly relevant information by considering the user's geographical location when voice or text input is received. For example, if the user is traveling, the reception unit can prioritize retrieving information related to their current location. Similarly, if the user is at home, the reception unit can prioritize retrieving information about nearby events. Furthermore, if the user is in a specific location, the reception unit can prioritize retrieving historical and cultural information related to that location. This allows for the prioritization of highly relevant information by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For instance, the reception unit can input the user's geographical location information into a generating AI and have the generating AI retrieve highly relevant information.

[0069] The reception unit can analyze the user's social media activity and retrieve relevant information when voice or text input is received. For example, the reception unit can prioritize retrieving information related to the user's most recent posts. It can also retrieve information related to posts from accounts the user follows. Furthermore, the reception unit can retrieve information related to the activities of groups and communities the user participates in. In this way, relevant information can be obtained by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI retrieve relevant information.

[0070] The generation unit can estimate the user's emotions and adjust the illustration's expression based on the estimated emotions. For example, if the user is sad, the generation unit can generate an illustration using soft colors and gentle expressions. If the user is excited, the generation unit can generate an illustration using vibrant colors and dynamic expressions. Furthermore, if the user is relaxed, the generation unit can generate an illustration using calm colors and simple expressions. By adjusting the illustration's expression based on the user's emotions, a more appropriate illustration can be generated. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the illustration's expression.

[0071] The generation unit can adjust the level of detail in the illustration based on the importance of the input content during generation. For example, in important scenes, the generation unit can depict detailed backgrounds and character expressions. In less important scenes, the generation unit can use simple backgrounds and character silhouettes. Furthermore, the generation unit can draw specific parts in detail to emphasize highly important information. In this way, important scenes can be emphasized by adjusting the level of detail in the illustration based on the importance of the input content. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the importance of the input content into the generation AI and have the generation AI perform the adjustment of the level of detail in the illustration.

[0072] The generation unit can apply different illustration generation algorithms depending on the category of the input content during generation. For example, in the case of the comedy category, the generation unit can apply an algorithm that emphasizes humorous expressions. In the case of the drama category, the generation unit can also apply an algorithm that emphasizes emotional expression. Furthermore, in the case of the action category, the generation unit can apply an algorithm that emphasizes dynamic scenes. By applying different illustration generation algorithms depending on the category of the input content, more appropriate illustrations can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the category of the input content into the generation AI and have the generation AI execute the application of the illustration generation algorithm.

[0073] The generation unit can estimate the user's emotions and adjust the style of the illustration based on the estimated emotions. For example, if the user is sad, the generation unit can generate an illustration with a soft touch. If the user is excited, the generation unit can generate an illustration with a strong touch. Furthermore, if the user is relaxed, the generation unit can generate an illustration with a simple and calm touch. In this way, by adjusting the style of the illustration based on the user's emotions, a more appropriate illustration can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform the adjustment of the illustration style.

[0074] The generation unit can determine the priority of illustrations based on the submission timing of the input content during generation. For example, the generation unit can prioritize processing input content with an approaching deadline. It can also postpone processing input content with a later submission deadline. Furthermore, the generation unit can dynamically adjust the processing priority according to the submission timing. This allows for deadline-based processing by determining the priority of illustrations based on the submission timing of the input content. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the submission timing of the input content into the generation AI and have the generation AI determine the priority of the illustrations.

[0075] The generation unit can adjust the order of illustrations based on the relevance of the input content during generation. For example, the generation unit can place important scenes first to clarify the flow of the story. It can also postpone scenes with low relevance. Furthermore, to maintain the consistency of the story, the generation unit can place highly relevant scenes consecutively. In this way, the consistency of the story can be maintained by adjusting the order of illustrations based on the relevance of the input content. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the relevance of the input content into the generation AI and have the generation AI perform the adjustment of the order of illustrations.

[0076] The output unit can estimate the user's emotions and adjust the display method of the web comic based on the estimated emotions. For example, if the user is sad, the output unit can display the web comic using a soft-colored background. If the user is excited, the output unit can display the web comic using a bright-colored background. Furthermore, if the user is relaxed, the output unit can display the web comic using a calm-colored background. This allows for a more appropriate display by adjusting the display method of the web comic based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input user emotion data into a generative AI and have the generative AI adjust the display method of the web comic.

[0077] The output unit can select the optimal display method by referring to the user's past browsing history when outputting. For example, the output unit can prioritize displaying manga in styles that the user has previously enjoyed reading. The output unit can also adjust the display method based on the genres of manga the user has previously viewed. Furthermore, the output unit can select the optimal display layout from the user's past browsing history. In this way, the optimal display method can be selected by referring to the user's past browsing history. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the user's past browsing history data into a generating AI and have the generating AI select the optimal display method.

[0078] The output unit can select the optimal display format when outputting, taking into account the user's device information. For example, if the user is using a smartphone, the output unit can provide a display format that matches the screen size. Furthermore, if the user is using a tablet, the output unit can provide a display format optimized for a larger screen. Additionally, if the user is using a desktop PC, the output unit can provide a display format optimized for a larger screen. This allows for the selection of the optimal display format by considering the user's device information. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the user's device information into a generating AI and have the generating AI select the optimal display format.

[0079] The output unit can estimate the user's emotions and adjust the display order of the web comic based on the estimated emotions. For example, if the user is sad, the output unit can display scenes that soothe the emotions first. Similarly, if the user is excited, the output unit can display scenes that calm the emotions first. Furthermore, if the user is relaxed, the output unit can display the story in its original order. This allows for a more appropriate display by adjusting the web comic's display order based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the output unit may be performed using AI, or not. For example, the output unit can input user emotion data into a generative AI and have the generative AI adjust the display order of the web comic.

[0080] The output unit can select the optimal display method when outputting, taking into account the user's geographical location information. For example, if the user is traveling, the output unit can prioritize displaying information related to the user's current location. Similarly, if the user is at home, the output unit can prioritize displaying information about nearby events. Furthermore, if the user is in a specific location, the output unit can prioritize displaying historical and cultural information related to that location. This allows the system to select the optimal display method by considering the user's geographical location information. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For instance, the output unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal display method.

[0081] The output unit can analyze the user's social media activity and select the optimal display method at the time of output. For example, the output unit can prioritize displaying information related to the user's recent posts. It can also display information related to posts from accounts the user follows. Furthermore, the output unit can display information related to the activities of groups and communities the user participates in. In this way, the optimal display method can be selected by analyzing the user's social media activity. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the user's social media activity data into a generating AI and have the generating AI select the optimal display method.

[0082] The management department can estimate the user's emotions and propose a subscription plan based on those emotions. For example, if the user is excited, the management department can propose a premium plan. If the user is relaxed, the management department can propose a standard plan. Furthermore, if the user is feeling anxious, the management department can propose a trial plan. This allows the management department to propose the most suitable subscription plan based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management department may be performed using AI or not. For example, the management department can input user emotion data into a generative AI and have the generative AI propose a subscription plan.

[0083] The management department can propose the optimal plan by referring to the user's past usage history during management. For example, the management department can propose a plan that includes features the user has frequently used in the past. The management department can also propose updates to plans the user has used in the past. Furthermore, the management department can customize and propose the optimal plan based on the user's past usage history. In this way, the optimal plan can be proposed by referring to the user's past usage history. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input the user's past usage history data into a generating AI and have the generating AI propose the optimal plan.

[0084] The management unit can estimate the user's emotions and adjust the subscription renewal timing based on the estimated emotions. For example, if the user is excited, the management unit can suggest an early renewal. If the user is relaxed, the management unit can suggest a normal renewal timing. Furthermore, if the user is feeling anxious, the management unit can extend the renewal timing. By adjusting the subscription renewal timing based on the user's emotions, it is possible to suggest renewal at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI or not using AI. For example, the management unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the subscription renewal timing.

[0085] The management department can propose the optimal plan during management, taking into account the user's geographical location information. For example, if the user lives in an urban area, the management department can propose a plan suited to urban areas. Similarly, if the user lives in a suburban area, the management department can propose a plan suited to suburban areas. Furthermore, if the user lives in a specific region, the management department can propose a plan tailored to that region. This allows the management department to propose the optimal plan by considering the user's geographical location information. Some or all of the above processing in the management department may be performed using AI, for example, or without AI. For example, the management department can input the user's geographical location information into a generating AI and have the generating AI propose the optimal plan.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The reception desk can automatically search for and suggest relevant images and videos based on the user's input. For example, if a user enters a travel experience, the reception desk can suggest images of scenery and videos of tourist spots at that travel destination. Similarly, if a user enters a cooking recipe, the reception desk can suggest videos demonstrating the cooking process and images of the finished dish. Furthermore, if a user enters a sports experience, the reception desk can suggest highlight videos and related images of that sport. This allows for the generation of richer content by providing visual information relevant to the user's input.

[0088] The administration department can send personalized notifications based on user usage. For example, if a user frequently uses a particular feature, it can notify them of new updates or tutorials related to that feature. It can also notify users of special offers or discounts to encourage re-use if they haven't used the service for a certain period. Furthermore, it can send reminders to users renew their subscriptions as they approach their expiration date. This allows for improved user engagement by sending appropriate notifications tailored to user usage.

[0089] The reception system can estimate the user's emotions and adjust the tone of the input based on those emotions. For example, if the user is angry, it can soften extreme expressions to create a calmer tone in the input. If the user is happy, it can add positive expressions to emphasize that emotion. Furthermore, if the user is sad, it can add comforting expressions. By adjusting the tone of the input based on the user's emotions, more appropriate communication can be achieved.

[0090] The reception desk can analyze a user's past input history and suggest the most suitable input template. For example, it can automatically generate and suggest templates that include phrases and expressions the user has frequently used in the past. It can also suggest templates appropriate for a specific theme when the user is making input related to that theme. Furthermore, it can suggest the most suitable template based on the input method the user has used in the past (voice, text, etc.). This allows for efficient input by analyzing the user's past input history.

[0091] The reception desk can analyze the user's voice tone and speed during voice input and suggest the optimal input method. For example, if the user is speaking quickly, the reception desk can suggest speaking more slowly. Similarly, if the user is speaking in a low voice, it can suggest speaking more clearly. Furthermore, if the user is speaking with an emotional tone, it can suggest an input method that reflects that emotion. By analyzing the user's voice tone and speed during voice input, it can support more effective input.

[0092] The reception desk can estimate the user's emotions and provide feedback on the input based on those estimated emotions. For example, if the user is feeling anxious, the reception desk can display an encouraging message. If the user is agitated, it can display a message encouraging them to calm down. Furthermore, if the user is sad, it can display a comforting message. This improves the user experience by providing appropriate feedback based on the user's emotions.

[0093] The reception desk can suggest relevant information based on the user's interests when they input voice or text. For example, if the user is interested in sports, it can suggest sports-related news and articles. If the user is interested in cooking, it can suggest recipes and cooking tips. Furthermore, if the user is interested in travel, it can suggest information about travel destinations and tourist attractions. In this way, by considering the user's interests, it can provide relevant information and improve the user experience.

[0094] The reception desk can adjust the input content, whether voice or text, taking into account the user's health condition. For example, if the user is tired, it can suggest a simpler input method. If the user is stressed, it can suggest a more relaxing input method. Furthermore, if the user is unwell, it can encourage them to input within their limits. This allows for the provision of more appropriate input methods and reduces the user's burden by considering their health condition.

[0095] The generation unit can estimate the user's emotions and adjust the color tones of the illustration based on those emotions. For example, if the user is sad, it can generate an illustration using soft colors. If the user is excited, it can generate an illustration using vibrant colors. Furthermore, if the user is relaxed, it can generate an illustration using calming colors. By adjusting the color tones of the illustration based on the user's emotions, it is possible to generate a more appropriate illustration.

[0096] The generation unit can adjust the style of the illustration based on the theme of the input content during generation. For example, if the theme is fantasy, it can generate an illustration using a fantastical style. If the theme is horror, it can generate an illustration using dark colors and a style that emphasizes fear. Furthermore, if the theme is comedy, it can generate an illustration using bright colors and a humorous style. In this way, by adjusting the style of the illustration based on the theme of the input content, it is possible to generate a more appropriate illustration.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The reception desk accepts voice or text input. If the user inputs their experience or what they want to convey via voice, speech recognition technology is used to convert the voice into text. If the user inputs their experience or what they want to convey via text, natural language processing technology is used to analyze the text. The reception desk accepts audio files in formats such as MP3 and WAV, and supports multiple languages ​​for text input, including Japanese and English. Step 2: The generation unit uses generation AI to analyze the information received by the reception unit and generate an illustration. The generation unit uses deep learning technology to generate the optimal illustration based on the input information and adjusts the style and resolution of the generated illustration. For example, it generates illustrations according to the user's preference, such as a realistic style or a cartoon style. Step 3: The output unit outputs the illustrations generated by the generation unit as a web comic. The output unit also generates HTML and CSS for displaying the generated illustrations on a web page, and can save them in PDF or image format. Furthermore, it automatically adjusts the number of pages and panel layout of the web comic to make it easy for users to view.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0101] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0102] Each of the multiple elements described above, including the reception unit, generation unit, output unit, and management unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and accepts voice or text input. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates illustrations using a generation AI. The output unit is implemented by the control unit 46A of the smart device 14 and outputs the generated illustrations as a web comic. The management unit is implemented by the specific processing unit 290 of the data processing unit 12 and manages the subscription model. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0106] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0107] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0109] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0110] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0111] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0112] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0113] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0114] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0117] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements described above, including the reception unit, generation unit, output unit, and management unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and accepts voice or text input. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates illustrations using a generation AI. The output unit is implemented by the control unit 46A of the smart glasses 214 and outputs the generated illustrations as a web comic. The management unit is implemented by the specific processing unit 290 of the data processing unit 12 and manages the subscription model. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the reception unit, generation unit, output unit, and management unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and accepts voice or text input. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates illustrations using a generation AI. The output unit is implemented by the control unit 46A of the headset terminal 314 and outputs the generated illustrations as web comics. The management unit is implemented by the specific processing unit 290 of the data processing unit 12 and manages the subscription model. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0143] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0144] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0148] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] Each of the multiple elements described above, including the reception unit, generation unit, output unit, and management unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives voice or text input. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates illustrations using a generation AI. The output unit is implemented by the control unit 46A of the robot 414 and outputs the generated illustrations as a web comic. The management unit is implemented by the specific processing unit 290 of the data processing unit 12 and manages the subscription model. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0152] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0154] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0155] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0156] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0160] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0161] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0162] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0163] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0164] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0165] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0167] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0168] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0169] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0170] (Note 1) A reception desk that accepts voice or text input, A generation unit analyzes the information received by the reception unit and generates an illustration, The system includes an output unit that outputs the illustrations generated by the generation unit as a web comic. A system characterized by the following features. (Note 2) It has a management department that manages the subscription model for monetization. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is It estimates the user's emotions and filters the input content based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is During voice input, background noise is automatically removed to obtain clear audio. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When users input voice or text, the system prioritizes retrieving highly relevant information by considering their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When a user enters text or voice, the system analyzes their social media activity and retrieves relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 9) The generating unit is The system estimates the user's emotions and adjusts the illustration's expression based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is During generation, the level of detail in the illustration is adjusted based on the importance of the input content. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is During generation, different illustration generation algorithms are applied depending on the category of the input content. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is It estimates the user's emotions and adjusts the illustration style based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is During generation, the priority of illustrations is determined based on when the input content was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is During generation, the order of the illustrations is adjusted based on the relevance of the input content. The system described in Appendix 1, characterized by the features described herein. (Note 15) The output unit is, The system estimates the user's emotions and adjusts how web comics are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The output unit is, When outputting, the system selects the optimal display method by referring to the user's past browsing history. The system described in Appendix 1, characterized by the features described herein. (Note 17) The output unit is, When outputting, the system selects the optimal display format considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The output unit is, The system estimates the user's emotions and adjusts the display order of web comics based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The output unit is, When outputting, the system selects the optimal display method considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 20) The output unit is, During output, the system analyzes the user's social media activity to select the optimal display method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned management department, It estimates the user's emotions and suggests a subscription plan based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 22) The aforementioned management department, During management, we refer to the user's past usage history to suggest the optimal plan. The system described in Appendix 2, characterized by the features described herein. (Note 23) The aforementioned management department, It estimates user sentiment and adjusts subscription renewal timing based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 24) The aforementioned management department, During management, the system proposes the optimal plan considering the user's geographical location. The system described in Appendix 2, characterized by the features described herein. [Explanation of symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that accepts voice or text input, A generation unit analyzes the information received by the reception unit and generates an illustration, The system includes an output unit that outputs the illustrations generated by the generation unit as a web comic. A system characterized by the following features.

2. It has a management department that manages the subscription model for monetization. The system according to feature 1.

3. The aforementioned reception unit is It estimates the user's emotions and filters the input content based on the estimated user emotions. The system according to feature 1.

4. The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system according to feature 1.

5. The aforementioned reception unit is During voice input, background noise is automatically removed to obtain clear audio. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is When users input voice or text, the system prioritizes retrieving highly relevant information by considering their geographical location. The system according to feature 1.

8. The aforementioned reception unit is When a user enters text or voice, the system analyzes their social media activity and retrieves relevant information. The system according to feature 1.

9. The generating unit is The system estimates the user's emotions and adjusts the illustration's expression based on those emotions. The system according to feature 1.

Images